Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Assistive intelligent environments for automatic health monitoring
Assistive intelligent environments for automatic health monitoring
Accurate activity recognition in a home setting
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Relational Transformation-based Tagging for Activity Recognition
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
kFOIL: learning simple relational kernels
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Common sense based joint training of human activity recognizers
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
TildeCRF: conditional random fields for logical sequences
ECML'06 Proceedings of the 17th European conference on Machine Learning
Efficiently inducing features of conditional random fields
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
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Hidden Markov Models (HMMs) are widely used in activity recognition. Ideally, the current activity should be determined using the vector of all sensor readings; however, this results in an exponentially large space of observations. The current fix to this problem is to assume conditional independence between individual sensors, given an activity, and factorizing the emission distribution in a naive way. In several cases, this leads to accuracy loss. We present an intermediate solution, viz., determining a mapping between each activity and conjunctions over a relevant subset of dependent sensors. The approach discovers features that are conjunctions of sensors and maps them to activities. This does away the assumption of naive factorization while not ruling out the possibility of the vector of all the sensor readings being relevant to activities. We demonstrate through experimental evaluation that our approach prunes potentially irrelevant subsets of sensor readings and results in significant accuracy improvements.